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Augmented CARDS: A machine learning approach to identifying triggers of climate change misinformation on Twitter (2404.15673v1)

Published 24 Apr 2024 in cs.LG

Abstract: Misinformation about climate change poses a significant threat to societal well-being, prompting the urgent need for effective mitigation strategies. However, the rapid proliferation of online misinformation on social media platforms outpaces the ability of fact-checkers to debunk false claims. Automated detection of climate change misinformation offers a promising solution. In this study, we address this gap by developing a two-step hierarchical model, the Augmented CARDS model, specifically designed for detecting contrarian climate claims on Twitter. Furthermore, we apply the Augmented CARDS model to five million climate-themed tweets over a six-month period in 2022. We find that over half of contrarian climate claims on Twitter involve attacks on climate actors or conspiracy theories. Spikes in climate contrarianism coincide with one of four stimuli: political events, natural events, contrarian influencers, or convinced influencers. Implications for automated responses to climate misinformation are discussed.

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References (42)
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Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Geiger, N., Swim, J.K.: Climate of silence: Pluralistic ignorance as a barrier to climate change discussion. Journal of Environmental Psychology 47, 79–90 (2016) Cook et al. [2017] Cook, J., Lewandowsky, S., Ecker, U.K.: Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. PloS one 12(5), 0175799 (2017) Ross Arguedas et al. [2022] Ross Arguedas, A.A., Badrinathan, S., Mont’Alverne, C., Toff, B., Fletcher, R., Nielsen, R.K.: “it’sa battle you are never going to win”: Perspectives from journalists in four countries on how digital media platforms undermine trust in news. Journalism Studies 23(14), 1821–1840 (2022) Allcott and Gentzkow [2017] Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J., Lewandowsky, S., Ecker, U.K.: Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. PloS one 12(5), 0175799 (2017) Ross Arguedas et al. [2022] Ross Arguedas, A.A., Badrinathan, S., Mont’Alverne, C., Toff, B., Fletcher, R., Nielsen, R.K.: “it’sa battle you are never going to win”: Perspectives from journalists in four countries on how digital media platforms undermine trust in news. Journalism Studies 23(14), 1821–1840 (2022) Allcott and Gentzkow [2017] Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ross Arguedas, A.A., Badrinathan, S., Mont’Alverne, C., Toff, B., Fletcher, R., Nielsen, R.K.: “it’sa battle you are never going to win”: Perspectives from journalists in four countries on how digital media platforms undermine trust in news. Journalism Studies 23(14), 1821–1840 (2022) Allcott and Gentzkow [2017] Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
  2. Geiger, N., Swim, J.K.: Climate of silence: Pluralistic ignorance as a barrier to climate change discussion. Journal of Environmental Psychology 47, 79–90 (2016) Cook et al. [2017] Cook, J., Lewandowsky, S., Ecker, U.K.: Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. PloS one 12(5), 0175799 (2017) Ross Arguedas et al. [2022] Ross Arguedas, A.A., Badrinathan, S., Mont’Alverne, C., Toff, B., Fletcher, R., Nielsen, R.K.: “it’sa battle you are never going to win”: Perspectives from journalists in four countries on how digital media platforms undermine trust in news. Journalism Studies 23(14), 1821–1840 (2022) Allcott and Gentzkow [2017] Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. 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[2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J., Lewandowsky, S., Ecker, U.K.: Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. PloS one 12(5), 0175799 (2017) Ross Arguedas et al. [2022] Ross Arguedas, A.A., Badrinathan, S., Mont’Alverne, C., Toff, B., Fletcher, R., Nielsen, R.K.: “it’sa battle you are never going to win”: Perspectives from journalists in four countries on how digital media platforms undermine trust in news. Journalism Studies 23(14), 1821–1840 (2022) Allcott and Gentzkow [2017] Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ross Arguedas, A.A., Badrinathan, S., Mont’Alverne, C., Toff, B., Fletcher, R., Nielsen, R.K.: “it’sa battle you are never going to win”: Perspectives from journalists in four countries on how digital media platforms undermine trust in news. Journalism Studies 23(14), 1821–1840 (2022) Allcott and Gentzkow [2017] Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
  3. Cook, J., Lewandowsky, S., Ecker, U.K.: Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. PloS one 12(5), 0175799 (2017) Ross Arguedas et al. [2022] Ross Arguedas, A.A., Badrinathan, S., Mont’Alverne, C., Toff, B., Fletcher, R., Nielsen, R.K.: “it’sa battle you are never going to win”: Perspectives from journalists in four countries on how digital media platforms undermine trust in news. Journalism Studies 23(14), 1821–1840 (2022) Allcott and Gentzkow [2017] Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ross Arguedas, A.A., Badrinathan, S., Mont’Alverne, C., Toff, B., Fletcher, R., Nielsen, R.K.: “it’sa battle you are never going to win”: Perspectives from journalists in four countries on how digital media platforms undermine trust in news. Journalism Studies 23(14), 1821–1840 (2022) Allcott and Gentzkow [2017] Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–236 (2017) Martens et al. [2018] Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. 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PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. 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[2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. 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[2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Martens, B., Aguiar, L., Gomez-Herrera, E., Mueller-Langer, F.: The digital transformation of news media and the rise of disinformation and fake news (2018) Tsfati et al. [2020] Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Tsfati, Y., Boomgaarden, H.G., Strömbäck, J., Vliegenthart, R., Damstra, A., Lindgren, E.: Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44(2), 157–173 (2020) Hsu and Thompson [2023] Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). Accessed 2023-02-08 Falkenberg et al. [2022] Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. 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Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? 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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hsu, T., Thompson, S.: Disinformation researchers raise alarms about a.i. chatbots. The New York Times (2023). 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[2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. 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[2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al.: Growing polarization around climate change on social media. Nature Climate Change 12(12), 1114–1121 (2022) Jang and Hart [2015] Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. 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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. 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[2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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[2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter big data. Global environmental change 32, 11–17 (2015) Pearce et al. [2014] Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. 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[2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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[2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. 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[2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Pearce, W., Holmberg, K., Hellsten, I., Nerlich, B.: Climate change on twitter: Topics, communities and conversations about the 2013 ipcc working group 1 report. PloS one 9(4), 94785 (2014) Anderson and Huntington [2017] Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Anderson, A.A., Huntington, H.E.: Social media, science, and attack discourse: How twitter discussions of climate change use sarcasm and incivility. Science Communication 39(5), 598–620 (2017) Effrosynidis et al. [2022] Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. 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[2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. 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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. 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Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. 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[2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Effrosynidis, D., Sylaios, G., Arampatzis, A.: Exploring climate change on twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters. Plos one 17(9), 0274213 (2022) Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018) Ecker et al. [2010] Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. 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[2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. 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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Ecker, U.K., Lewandowsky, S., Tang, D.T.: Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition 38, 1087–1100 (2010) Hassan et al. [2015] Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. 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Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? 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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. 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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
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Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. 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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Hassan, N., Adair, B., Hamilton, J.T., Li, C., Tremayne, M., Yang, J., Yu, C.: The quest to automate fact-checking. In: Proceedings of the 2015 Computation+ Journalism Symposium (2015). Citeseer Andersen and Søe [2020] Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Andersen, J., Søe, S.O.: Communicative actions we live by: The problem with fact-checking, tagging or flagging fake news–the case of facebook. European Journal of Communication 35(2), 126–139 (2020) Guo et al. [2022] Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Transactions of the Association for Computational Linguistics 10, 178–206 (2022) Boussalis and Coan [2016] Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. 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[2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. 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[2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. 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Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? 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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. 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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. 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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global Environmental Change 36, 89–100 (2016) Farrell [2016] Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. 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[2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. 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[2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. 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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Farrell, J.: Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences 113(1), 92–97 (2016) Stecula and Merkley [2019] Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. Frontiers in Communication 4, 6 (2019) Alhindi et al. [2023] Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Stecula, D.A., Merkley, E.: Framing climate change: Economics, ideology, and uncertainty in american news media content from 1988 to 2014. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Alhindi, T., Chakrabarty, T., Musi, E., Muresan, S.: Multitask Instruction-based Prompting for Fallacy Recognition (2023) Jin et al. [2022] Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. 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[2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B.: Logical Fallacy Detection (2022) Zanartu et al. [2023] Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. 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Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. 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Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zanartu, F., Cook, J., Wagner, M., Gallego, J.G.: Automatic detection of fallacies in climate change misinformation (2023) Coan et al. [2021] Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Coan, T., Boussalis, C., Cook, J., Nanko, M.: Computer-assisted detection and classification of misinformation about climate change (2021) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. 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[2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. 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Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. 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[2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. 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[2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? 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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. 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[2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423 Liu et al. [2019] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) He et al. [2020] He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. 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Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
  27. He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. ArXiv abs/2006.03654 (2020) [30] Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
  28. Chris Bauch, University of Waterloo. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset Brüggemann [2023] Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Brüggemann, M..S.R.: Online media monitor on climate change (omm): Analysis of global tweets and online media coverage. (2023) [32] Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Smith, S.: Biden under pressure to declare climate emergency after manchin torpedoes bill. The Guardian. Accessed 2023-11-10 Luscombe [2022a] Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. 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Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
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Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? 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Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. 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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
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The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). 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[2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. 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[2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31
  31. Luscombe, R.: Hurricane ian: more than 2m without power as florida hit with ‘catastrophic’ wind and rain. The Guardian (2022). Accessed 2023-06-02 Luscombe [2022b] Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Luscombe, R.: Hurricane ian: ‘catastrophic’ damage in florida as storm heads to south carolina. The Guardian (2022) van der Zee and Horton [2022] Zee, B., Horton, H.: Cop27 day one: Un chief warns world is ‘on highway to climate hell’ – as it happened. The guardian (2022) Xia et al. [2021] Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? 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Accessed: 2023-05-31 Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? 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Accessed: 2023-05-31 Xia, Y., Chen, T.H.Y., Kivelä, M.: Spread of tweets in climate discussions: A case study of the 2019 nobel peace prize announcement. Nordic journal of media studies 3(1), 96–117 (2021) Bennett [2012] Bennett, W.L.: The personalization of politics: Political identity, social media, and changing patterns of participation. The ANNALS of the American Academy of Political and Social Science 644(1), 20–39 (2012) https://doi.org/10.1177/0002716212451428 https://doi.org/10.1177/0002716212451428 Kissas [2022] Kissas, A.: Populist everyday politics in the (mediatized) age of social media: The case of instagram celebrity advocacy. New Media & Society 0(0), 14614448221092006 (2022) https://doi.org/10.1177/14614448221092006 https://doi.org/10.1177/14614448221092006 Rathje et al. [2021] Rathje, S., Bavel, J.J.V., Linden, S.: Out-group animosity drives engagement on social media. 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Proceedings of the National Academy of Sciences 118(26), 2024292118 (2021) https://doi.org/10.1073/pnas.2024292118 https://www.pnas.org/doi/pdf/10.1073/pnas.2024292118 Barrie [2023] Barrie, C.: Did the musk takeover boost contentious actors on twitter? Harvard Kennedy School Misinformation Review (2023) [41] Deny, deceive, delay: Exposing new trends in climate mis- and disinformation at cop27 (vol 2). Institute for Strategic Dialogue (2023) [42] Climate of misinformation: Ranking big tech. Climate Action Against Disinformation and Friends of the Earth and Greenpeace (2023) Cook [2020] Cook, J.: Deconstructing climate science denial. Research handbook on communicating climate change, 62–78 (2020) [44] Twitter Climate Change Sentiment Dataset. https://www.kaggle.com/datasets/edqian/twitter-climate-change-sentiment-dataset. Accessed: 2023-05-31 Barrie, C.: Did the musk takeover boost contentious actors on twitter? 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